Add to ORKGThis article is concerned with nonparametric inference for quantiles from a Bayesian perspective, using the Dirichlet process. The posterior distribution for quantiles is characterized, enabling also explicit formulae for posterior mean and variance. Unlike the Bayes estimator for the distribution function, our Bayes estimator for the quantile function is a smooth curve. A Bernstein-von Mises type theorem is given, exhibiting the limiting posterior distribution of the quantile process. Links to kernel smoothed quantile estimators are provided. As a side product we develop an automatic nonparametric density estimator, free of smoothing parameters, with support exactly matching that of the data range. Nonparametric Bayes estimators are als...
The classical theory of linear models focuses on the conditional mean function, i.e. the function th...
The classical theory of linear models focuses on the conditional mean function, i.e. the function th...
Nonparametric Bayesian inference has widespread applications in statistics and machine learning. In ...
We develop a Bayesian method for nonparametric model–based quantile regression. The approach in-volv...
A family of nonparametric prior distributions which extends the Dirichlet process is introduced and ...
The Bayesian nonparametric inference requires the construction of priors on infinite dimensional spa...
Bayesian nonparametric inference is a relatively young area of research and it has recently undergon...
Alternatives to the Dirichlet prior for multinomial probabilities are explored. The Dirichlet prior ...
Abstract. Bayesian nonparametric inference is a relatively young area of research and it has recentl...
This book focuses on the properties associated with the Dirichlet process, describing its use a prio...
This book presents a systematic and comprehensive treatment of various prior processes that have bee...
Pólya trees fix partitions and use random probabilities in order to construct random probability mea...
In this talk I will discuss some recent progress in Bayesian nonparametric modeling and inference. ...
The definition and investigation of general classes of non-parametric priors has recently been an ac...
The definition and investigation of general classes of non-parametric priors has recently been an ac...
The classical theory of linear models focuses on the conditional mean function, i.e. the function th...
The classical theory of linear models focuses on the conditional mean function, i.e. the function th...
Nonparametric Bayesian inference has widespread applications in statistics and machine learning. In ...
We develop a Bayesian method for nonparametric model–based quantile regression. The approach in-volv...
A family of nonparametric prior distributions which extends the Dirichlet process is introduced and ...
The Bayesian nonparametric inference requires the construction of priors on infinite dimensional spa...
Bayesian nonparametric inference is a relatively young area of research and it has recently undergon...
Alternatives to the Dirichlet prior for multinomial probabilities are explored. The Dirichlet prior ...
Abstract. Bayesian nonparametric inference is a relatively young area of research and it has recentl...
This book focuses on the properties associated with the Dirichlet process, describing its use a prio...
This book presents a systematic and comprehensive treatment of various prior processes that have bee...
Pólya trees fix partitions and use random probabilities in order to construct random probability mea...
In this talk I will discuss some recent progress in Bayesian nonparametric modeling and inference. ...
The definition and investigation of general classes of non-parametric priors has recently been an ac...
The definition and investigation of general classes of non-parametric priors has recently been an ac...
The classical theory of linear models focuses on the conditional mean function, i.e. the function th...
The classical theory of linear models focuses on the conditional mean function, i.e. the function th...
Nonparametric Bayesian inference has widespread applications in statistics and machine learning. In ...